Machine learning labeling platform for enabling automatic authorization of human work assistance

ABSTRACT

Systems and methods for dynamically assessing property damage by determining whether and how to leverage a crowdsourcing marketplace are provided. According to certain aspects, a server computer may receive a set of media depicting property damage, and may analyze the set of media using a machine learning model to estimate a type and amount of the property damage. The server computer may also determine whether and how to leverage a set of additional individuals to provide a set of assessments for the property damage and, based on the set of assessments provided by the set of additional individuals, the server computer may automatically facilitate a work order request to address the property damage.

FIELD

The present disclosure is directed to using machine learning todynamically assess property damage. More particularly, the presentdisclosure is directed to platforms and technologies for determiningwhether and how to employ a crowdsourcing marketplace to supplement amachine learning analysis of a set of media, and automaticallygenerating work orders based on the holistic media analysis.

BACKGROUND

Machine learning is an algorithmic approach to data analysis where aprocessing system builds a mathematical model based on sample trainingdata, and uses the mathematical model to make predictions or decisionsfrom additional datasets without being explicitly programmed to performthe tasks. As a result, machine learning techniques are able to learnfrom data, identify patterns, and make determinations with minimal or nohuman intervention.

A machine learning system may be used for various tasks andapplications. However, certain applications of machine learning modelsare inefficient in resolving the underlying tasks largely due to modelinaccuracies. For example, using machine learning models to assessproperty damage in an attempt to resolve property insurance claims isoften inaccurate. Additionally, current methods of addressing thesemodel inaccuracies by manually adjusting model parameters for generatingadditional accurate models interrupts the business process, among otherdrawbacks. This interruption unfortunately also requires mass datacuration, which is expensive, wastes data, and adds a redundant step tothe process of generating models, with minimal payoff regardingincreased model accuracy.

Accordingly, there is an opportunity for platforms and techniques thatimprove on existing techniques for using machine learning models forvarious applications.

SUMMARY

In an embodiment, a computer-implemented method of using a machinelearning model to automatically facilitate work orders is provided. Themethod may include: receiving, from an electronic device, a set of mediadepicting a property; analyzing, by a processor using a machine learningmodel, the set of media data, including: determining (i) an amount ofdamage to the property depicted in the set of media, and (ii) a type ofdamage to the property depicted in the set of media, and assessing aconfidence level for the amount of damage, wherein the confidence levelis below a threshold level; in response to analyzing the set of mediadata using the machine learning model, availing the set of media formanual review, including: determining, based on the property, a set ofindividuals needed to manually assess the set of media, each of the setof individuals having a qualification, availing the set of media forreview by the set of individuals, receiving a set of assessments of theset of media by the set of individuals, calculating an updated amount ofdamage to the property based on the set of assessments, and updating themachine learning model with the updated amount of damage to theproperty; determining, by the processor, a service provider equipped toaddress the type of damage to the property; and transmitting, to theservice provider via a computer network, an electronic work orderrequest indicating the updated amount of damage and the type of damage.

In another embodiment, a system for automatically facilitating workorders is provided. The system may include a transceiver, a memorystoring a set of instructions and a machine learning model, and aprocessor interfaced with the transceiver and the memory. The processormay be configured to execute the set of instructions to cause theprocessor to: receive, from an electronic device via the transceiver, aset of media depicting a property, analyze, using the machine learningmodel, the set of media data, including: determine (i) an amount ofdamage to the property depicted in the set of media, and (ii) a type ofdamage to the property depicted in the set of media, and assess aconfidence level for the amount of damage, wherein the confidence levelis below a threshold level, in response to analyzing the set of mediadata using the machine learning model, avail the set of media for manualreview, including: determine, based on the property, a set ofindividuals needed to manually assess the set of media, each of the setof individuals having a qualification, avail the set of media for reviewby the set of individuals, receive a set of assessments of the set ofmedia by the set of individuals, calculate an updated amount of damageto the property based on the set of assessments, and update, in thememory, the machine learning model with the updated amount of damage tothe property, determine a service provider equipped to address the typeof damage to the property, and transmit, to the service provider via thetransceiver, an electronic work order request indicating the updatedamount of damage and the type of damage.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A depicts an overview of components and entities associated withthe systems and methods, in accordance with some embodiments.

FIG. 1B depicts a detailed representation of certain componentsconfigured to facilitate the systems and methods, in accordance withsome embodiments.

FIG. 2 depicts a signal diagram of certain components andfunctionalities associated therewith, in accordance with someembodiments.

FIGS. 3A, 3B, 4A, and 4B depict example interfaces associated withreviewing, accepting, and submitting work orders, in accordance withsome embodiments.

FIG. 5 is an example flowchart associated with using machine learningmodels to analyze a set of media and facilitate work orders, inaccordance with some embodiments.

DETAILED DESCRIPTION

The present embodiments may relate to, inter alia, platforms andtechnologies for using a machine learning model to dynamically estimateproperty damage depicted in media submitted by an electronic device.According to certain aspects, systems and methods may compare aconfidence level associated with the estimated property damage to athreshold level. Based on the comparison, the systems and methods maydetermine how to supplement the analysis using crowdsourced electronicdevices, which may provide supplemental property damage assessments. Thesystems and methods may use the supplemental property damage assessmentsto update the machine learning model for subsequent data analyses.Additionally, the systems and methods may facilitate work order requestswith service providers equipped to address the depicted damage. A userof the electronic device that submitted the media may reject or acceptthe work order request accordingly.

The systems and methods therefore offer numerous benefits. Inparticular, by comparing a confidence level of an estimated damageamount to a threshold level, the systems and methods may effectivelydetermine when and how to employ crowdsourced resources for asupplemental damage assessment. The crowdsourced resources may provide adamage assessment that is more accurate than the assessment originallydetermined by the machine learning model. Therefore, the systems andmethods may update the machine learning model with more accurateassessments to be used in subsequent media analyses. Additionally, thesystems and methods may use accurate damage assessments to automaticallyfacilitate work order requests such that users are afforded the benefitsof being matched to appropriate service providers and having visibilityinto the status of work orders. It should be appreciated that additionalbenefits are envisioned.

The systems and methods discussed herein address a challenge that isparticular to damage assessment applications. In particular, thechallenge relates to a difficulty in accurately assessing damage toproperties and how to effectively resolve such situations.Conventionally, designated individuals manually review and assess damageto property, or machine learning models output a damage assessment thatmay be inaccurate. However, these conventional techniques are often timeconsuming, ineffective, expensive, and/or inaccurate. The systems andmethods offer improved capabilities to solve these problems bydynamically determining when and how to leverage crowdsourced resourcesto provide supplemental assessments to machine learning model analyses,and interfacing with service providers to dynamically facilitate workorder requests. Further, because the systems and methods employcommunication between and among multiple devices, the systems andmethods are necessarily rooted in computer technology in order toovercome the noted shortcomings that specifically arise in the realm ofdamage assessment applications.

FIG. 1A illustrates an overview of a system 100 of components configuredto facilitate the systems and methods. It should be appreciated that thesystem 100 is merely an example and that alternative or additionalcomponents are envisioned.

As illustrated in FIG. 1A, the system 100 may include a set ofelectronic devices 101, 102, 103. Each of the electronic devices 101,102, 103 may be any type of electronic device such as a mobile device(e.g., a smartphone), desktop computer, notebook computer, tablet,phablet, GPS (Global Positioning System) or GPS-enabled device, smartwatch, smart glasses, smart bracelet, wearable electronic, PDA (personaldigital assistant), pager, computing device configured for wirelesscommunication, and/or the like. Generally, each of the electronicdevices 101, 102, 103 may be operated by an individual or person(generally, a user) having an association with a property, for example avehicle, home, or other type of physical property capable of being ownedor used. For example, a user may be a policyholder of an insurancepolicy for a vehicle.

In operation, the user may operate one of the devices 101, 102, 103 toinput data or information associated with a property in the event thatthe property is damaged. In particular, the user may input (e.g., via akeyboard or dictation) a description of the damage to the property.Additionally, the user may use the corresponding device 101, 102, 103 tocapture (or access) digital images and/or videos of the property.Generally, the term “media” or “set of media” may be used throughout todescribe visual content (e.g., digital images or digital videos)depicting a property. A given set of media may include a set of digitalimages or videos depicting various views and perspectives of a givenproperty, where the given property may have damage to certain portionsor areas to varying degrees.

The electronic devices 101, 102, 103 may communicate with a servercomputer 115 via one or more networks 110. The server computer 115 maybe associated with an entity such as a company, business, corporation,or the like, which manages policies, accounts, or the like forproperties associated with users. For example, the server computer 115may be associated with an insurance company that offers home and/orvehicle insurance policies held by users of the electronic devices 101,102, 103. The electronic devices 101, 102, 103 may transmit orcommunicate, via the network(s) 110, the set of media and any anothercaptured or inputted information or data to the server computer 115.

In embodiments, the network(s) 110 may support any type of datacommunication via any standard or technology including various wide areanetwork or local area network protocols (e.g., GSM, CDMA, VoIP, TDMA,WCDMA, LTE, EDGE, OFDM, GPRS, EV-DO, UWB, Internet, IEEE 802 includingEthernet, WiMAX, Wi-Fi, Bluetooth, and others). Further, in embodiments,the network(s) 110 may be any telecommunications network that maysupport a telephone call between the electronic devices 101, 102, 103and the server computer 115.

The system 100 further includes a set of additional devices 106, 107,108 that may be operable by a set of additional users. According toembodiments, the set of additional devices 106, 107, 108 may be devicescapable of supporting an Internet crowdsourcing marketplace to enableindividuals and businesses (such as businesses associated with theserver computer 115) to coordinate human labor to perform tasks thatcomputers are either unable to do or tasks that may supplement taskscertain tasks performed by computers. An example of such a crowdsourcingmarketplace is Amazon Mechanical Turk (MTurk). The server computer 115may communicate with the set of additional devices 106, 107, 108 via thenetwork(s).

The system 100 additionally includes a set of service providers 120,each of which may represent a business, entity, corporation, or the likethat may be equipped to handle or address property repairs, where eachservice provider 120 may have a central server or other computing devicecapable of communication with the server computer 115 via the network(s)110. For example, one of the service providers 120 may represent avehicle mechanic having an electronic device capable of communicationwith the server computer 115. For further example, one of the serviceproviders 120 may be a vehicle dealership having a service departmentwith a computing device(s) capable of communication with the servercomputer 115.

The server computer 115 may be configured to interface with or support amemory or storage 113 capable of storing various data, such as in one ormore databases or other forms of storage. According to embodiments, thestorage 113 may store data or information associated with any machinelearning models that are generated by the server computer 115, any setsof media received from the electronic devices 101, 102, 103, anyassessment data received from the set of additional devices 106, 107,108, and/or any other pertinent data.

According to embodiments, the server computer 115 may employ variousmachine learning techniques, calculations, algorithms, and the like togenerate and maintain a machine learning model associated with mediadepicting properties that may be damaged. The server computer 115 mayinitially train the machine learning model using a set of training data(or in some cases, may not initially train the machine learning model).In an implementation, the set of training data may be generated by oneor more of the additional devices 106, 107, 108, and transmitted to theserver computer 115. Generally, the set of training data may include aset of images and/or video depicting damage to various properties (e.g.,vehicles), where the set of training data may include a set of labelsinput by a set of users who review the set of images and/or video. Thestorage 113 may store the trained machine learning model.

In operation, the server computer 115 may analyze the set of mediareceived from one or more of the electronic devices 101, 102, 103 usingthe machine learning model. In analyzing the set of media, the servercomputer 115 may generate an output that indicates or is descriptive ofany property damage depicted in the set of media, as well as aconfidence level. The server computer 115 may compare the confidencelevel to a set threshold level, and may determine whether and how totransmit the set of media to one or more of the additional devices 106,107, 108 for a supplemental assessment. After the users review the setof media and input a supplemental assessment(s) of the property damage,the one or more additional devices 106, 107, 108 may transmit thesupplemental assessment(s) to the server computer 115, which may analyzethe supplemental assessment(s) and update the machine learning modelaccordingly. Additionally, the server computer 115 may generate andtransmit a work order to the service provider 120 to facilitate repairof the property damage depicted in the set of media. Thesefunctionalities are further described with respect to FIG. 1B and FIG.2.

Although depicted as a single server computer 115 in FIG. 1A, it shouldbe appreciated that the server computer 115 may be in the form of adistributed cluster of computers, servers, machines, or the like. Inthis implementation, the entity may utilize the distributed servercomputer(s) 115 as part of an on-demand cloud computing platform.Accordingly, when the electronic devices 101, 102, 103 and additionaldevices 106, 107, 108 interface with the server computer 115, theelectronic devices 101, 102, 103 and additional devices 106, 107, 108may actually interface with one or more of a number of distributedcomputers, servers, machines, or the like, to facilitate the describedfunctionalities. Additionally, although three (3) electronic devices101, 102, 103, three (3) additional devices 106, 107, 108, and one (1)server computer 115 are depicted in FIG. 1A, it should be appreciatedthat greater or fewer amounts are envisioned.

FIG. 1B an example environment 150 in which a set(s) of media 151 isprocessed into media analysis output data and/or work order data 152 viaa media analysis platform 155, according to embodiments. The mediaanalysis platform 155 may be implemented on any computing device,including the server computer 115 (or in some implementations, one ormore of the electronic devices 101, 102, 103) as discussed with respectto FIG. 1A. Components of the computing device may include, but are notlimited to, a processing unit (e.g., processor(s) 156), a system memory(e.g., memory 157), and a system bus 158 that couples various systemcomponents including the memory 157 to the processor(s) 156.

In some embodiments, the processor(s) 156 may include one or moreparallel processing units capable of processing data in parallel withone another. The system bus 158 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, or a local bus, and may use any suitable bus architecture. By wayof example, and not limitation, such architectures include the IndustryStandard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus,Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA)local bus, and Peripheral Component Interconnect (PCI) bus (also knownas Mezzanine bus).

The media analysis platform 155 may further include a user interface 153configured to present content (e.g., the set(s) of media 151 and anyoutput data associated with analysis of the set(s) of media 151).Additionally, a user may make selections to the content via the userinterface 153, such as to navigate through different information, selectand review certain machine learning model output, and/or other actions.The user interface 153 may be embodied as part of a touchscreenconfigured to sense touch interactions and gestures by the user.Although not shown, other system components communicatively coupled tothe system bus 158 may include input devices such as a cursor controldevice (e.g., a mouse, trackball, touch pad, etc.) and keyboard (notshown). A monitor or other type of display device may also be connectedto the system bus 158 via an interface, such as a video interface. Inaddition to the monitor, computers may also include other peripheraloutput devices such as a printer, which may be connected through anoutput peripheral interface (not shown).

The memory 157 may include a variety of computer-readable media.Computer-readable media may be any available media that can be accessedby the computing device and may include both volatile and nonvolatilemedia, and both removable and non-removable media. By way ofnon-limiting example, computer-readable media may comprise computerstorage media, which may include volatile and nonvolatile, removable andnon-removable media implemented in any method or technology for storageof information such as computer-readable instructions, routines,applications, data structures, program modules or other data. One of theapplications may be a media analysis application 160 configured toanalyze the set(s) of media 151 using a machine learning model.

Computer storage media may include, but is not limited to, RAM, ROM,EEPROM, FLASH memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can accessed by the processor 156 of the computing device.

The media analysis platform 155 may operate in a networked environmentand communicate with one or more remote platforms or devices via anetwork(s) 162, such as a local area network (LAN), a wide area network(WAN), telecommunications network, or other suitable network. Inparticular, the media analysis platform 155 may interface with at leastone additional device 165 (such as one of the additional devices 106,107, 108 as discussed with respect to FIG. 1A), which may support anInternet crowdsourcing marketplace for assessing characteristicsassociated with the set(s) of media 151. In operation, the mediaanalysis platform 155 may transmit the set(s) of media 151 and anysupplemental data to the additional device 165 for a supplementalanalysis of the set(s) of media 151. Although a single additional device165 is depicted in FIG. 1B, it should be appreciated that there may bemultiple additional devices 165.

The additional device 165 may include a processing unit (e.g.,processor(s) 166), a system memory (e.g., memory 168), and a system bus171 that couples various system components including the memory 168 tothe processor(s) 166. In some embodiments, the processor(s) 166 mayinclude one or more parallel processing units capable of processing datain parallel with one another. The system bus 171 may be any of severaltypes of bus structures including a memory bus or memory controller, aperipheral bus, or a local bus, and may use any suitable busarchitecture. By way of example, and not limitation, such architecturesinclude the Industry Standard Architecture (ISA) bus, Micro ChannelArchitecture (MCA) bus, Enhanced ISA (EISA) bus, Video ElectronicsStandards Association (VESA) local bus, and Peripheral ComponentInterconnect (PCI) bus (also known as Mezzanine bus).

The additional device 165 may further include a user interface 167configured to present content (e.g., the set(s) of media 151).Additionally, a user may make selections via the user interface 167,such as to navigate through different information, select and reviewcertain machine learning model output, provide damage assessments,and/or other actions. The user interface 167 may be embodied as part ofa touchscreen configured to sense touch interactions and gestures by theuser. Although not shown, other system components communicativelycoupled to the system bus 171 may include input devices such as a cursorcontrol device (e.g., a mouse, trackball, touch pad, etc.) and keyboard(not shown). A monitor or other type of display device may also beconnected to the system bus 171 via an interface, such as a videointerface. In addition to the monitor, the additional device 165 mayalso include other peripheral output devices such as a printer, whichmay be connected through an output peripheral interface (not shown).

The memory 168 may include a variety of computer-readable media.Computer-readable media may be any available media that can be accessedby the computing device and may include both volatile and nonvolatilemedia, and both removable and non-removable media. By way ofnon-limiting example, computer-readable media may comprise computerstorage media, which may include volatile and nonvolatile, removable andnon-removable media implemented in any method or technology for storageof information such as computer-readable instructions, routines,applications, data structures, program modules or other data. One of theapplications may be a media analysis application 169 configured to causethe user interface 167 to present the set(s) of media 151 for review bythe user. The memory 168 may additional store other data 170 that, forexample, the processor 166 may use in operating the media analysisapplication 169.

Computer storage media may include, but is not limited to, RAM, ROM,EEPROM, FLASH memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can accessed by the processor 166.

The media analysis platform 155 may store, as machine learning data 164,any data associated with machine learning models and/or analyzing theset(s) of media 151 using the machine learning models. Additionally, themedia analysis application 160 may employ machine learning techniquessuch as, for example, a regression analysis (e.g., a logisticregression, linear regression, or polynomial regression), k-nearestneighbors, decision trees, random forests, boosting, neural networks,support vector machines, deep learning, reinforcement learning, latentsemantic analysis, Bayesian networks, or the like. Generally, the mediaanalysis platform 155 may support various supervised and/or unsupervisedmachine learning techniques. In an embodiment, the media analysisapplication 160 may initially train a machine learning model withtraining data, and store the resulting machine learning model as machinelearning data 163. In another embodiment, the media analysis application160 may generate and update the machine learning model, and thecorresponding machine learning data 163, based on the received set(s) ofmedia 151 and/or any results received form the additional device 165.Functionalities of the media analysis platform 155 and the additionaldevice 165 are further described with respect to FIG. 2.

In general, a computer program product in accordance with an embodimentmay include a computer usable storage medium (e.g., standard randomaccess memory (RAM), an optical disc, a universal serial bus (USB)drive, a big data processing engine, a NoSQL repository, or the like)having computer-readable program code embodied therein, wherein thecomputer-readable program code may be adapted to be executed by theprocessors 156, 166 (e.g., working in connection with an operatingsystems) to facilitate the functions as described herein. In thisregard, the program code may be implemented in any desired language, andmay be implemented as machine code, assembly code, byte code,interpretable source code or the like (e.g., via Golang, Python, Scala,C, C++, Java, Actionscript, Objective-C, Javascript, CSS, XML). In someembodiments, the computer program product may be part of a cloud networkof resources. Generally, each of the data 151 and the data 152 may beembodied as any type of electronic document, file, template, etc., thatmay include various textual and visual content, and may be stored inmemory as program data in a hard disk drive, magnetic disk and/oroptical disk drive in the media analysis platform 155 and/or theadditional device 165.

FIG. 2 depicts a signal diagram 200 including various functionalities ofthe systems and methods. The signal diagram 200 includes a user device205 (such as one of the user devices 101, 102, 103 as discussed withrespect to FIG. 1A), a service provider 220 (such as the serviceprovider(s) 120 as discussed with respect to FIG. 1A), a server computer215 (such as the server computer 115 as discussed with respect to FIG.1A), and a set of additional devices 225 (such as the set of additionaldevices 106, 107, 108 as discussed with respect to FIG. 1A). It shouldbe appreciated that the amount of additional devices 225 may vary, whereeach additional device 225 may be operated by a human (generally, auser). Although the functionalities of FIG. 2 are described with respectto assessing media depicting a vehicle, it should be appreciated thatthe functionalities may be applied to other property types (e.g., homesand other physical properties).

The signal diagram 200 may start when the set of additional devices 225assess (226) a set of training data. Generally, the set of training datamay include real-world or simulated media (e.g., images, videos,alphanumeric content, and/or the like) that depicts various property(e.g., vehicles or homes) having various levels of damage. In assessingthe set of training data, the corresponding user of the additionaldevice 225 may apply or otherwise specify a label or tag to each item ofmedia. For example, the set of training data may include a series ofimages depicting various degrees of damage to various vehicles, and theusers of the additional devices 225 may specify labels descriptive ofthe vehicles and/or of the damage to the vehicles (e.g., “sedan,”“minivan,” “SUV,” “total loss,” “body damage,” “broken windshield,”“flat tire,” etc.).

The set of additional devices 225 may transmit (228) the labeledtraining data to the server computer 215. As a result, the servercomputer 215 may build and store (230) a machine learning model usingthe labeled training data. The machine learning model may thus bedescriptive of various types and degrees of damage to various types ofproperties (e.g., different vehicle make, models, and types), where theserver computer 215 may use the machine learning model to assessadditional media depicting additional properties. The machine learningmodel may additionally associate damage repair amounts with the types ofdamage, where the damage repair amounts may be based on the type and/orage of the property.

The user device 205 may transmit (232) a set of media to the servercomputer 215, where the set of media transmitted by the user device 205may depict a property associated with a user of the user device 205, andmay depict damage to the property. In operation, the transmission of theset of media may be in association with the user filing an insuranceclaim in response to the occurrence of damage to the property. Inembodiments, the set of media may be supplemented with a description ofthe damage inputted by the user, and/or with information that identifiesthe user and/or the property (e.g., a make, model, and year of thevehicle, an odometer reading of the vehicle, etc.). For example, the setof media may be five (5) images of a damaged SUV, with appended textdescribing damage to the driver's side doors of the SUV.

The server computer 215 may analyze (234) the set of media transmittedfrom the user device 205. In particular, the server computer 215 may usethe stored machine learning model to analyze the set of media. Inanalyzing the set of media using the machine learning model, the servercomputer 215 may generate an output that estimates a type and amount ofdamage depicted in the set of media as well as a confidence level forthe estimated type and amount of damage. For example, the servercomputer 215 may output that the transmitted set of media depicts damageto the rear driver's side door, having an estimated repair cost of$1,500, and with a confidence level of 60%. In some implementations, theserver computer 215 may additionally output a make, model, and year ofthe depicted vehicle (such as in situations in which this information isnot included in the originally-submitted information). It should beappreciated that the server computer 215 may determine the confidencelevel using a variety of techniques or calculations, which may be basedon a quality level and/or completeness of the data received from theuser device 205.

According to embodiments, there may be a confidence threshold levelassociated with the output from the machine learning model, where theconfidence threshold level may be a default value or specified by anadministrator associated with the server computer 215. Additionally,there may be multiple confidence threshold levels that may be based onsuch factors as make, model, and year of a vehicle, amount of miles onthe odometer, the estimated amount of damage, and/or other factors. Forexample, a first confidence threshold level may be 60% for a vehiclethat is over ten (10) years old and having an estimated damage amount of$2,000 and under; and another confidence threshold level may be 90% fora vehicle that is less than two (2) years old and having an estimateddamage amount of $10,000 and over. The server computer 215 may store theconfidence threshold levels as well as a set of rules for applying theconfidence threshold levels.

The server computer 215 may determine (236) whether the confidence levelthat is output from the machine learning model analysis at least meetsthe corresponding confidence level threshold. If the outputtedconfidence level at least meets the corresponding confidence levelthreshold (“YES”), processing may proceed to (248). In contrast, if theoutputted confidence level does not meet the corresponding confidencelevel (“NO”), processing may proceed to (238).

In embodiments, the server computer 215 may additionally oralternatively facilitate employ an audit threshold in association withthe output from the machine learning model. Generally, a portion of theoutputs from the machine learning model may have a high confidence level(e.g., greater than 90%) but be inaccurate or wrong, where the auditthreshold may be used to validate (i.e., spot check) certain outputsfrom the machine learning model (e.g., those with a confidence levelthat exceeds a threshold level, for example 90%) to further improveaccuracy. The audit threshold may be a default amount or may bespecified by an administrator associated with the server computer 215.For example, if the audit threshold is 5% and the applicability is aconfidence level of at least 95%, the server computer 215 may “flag” 5%of the machine learning model outputs having a confidence level of atleast 95%.

If the output from the machine learning model passes the audit threshold(i.e., is not flagged), processing may proceed to (248). In contrast, ifthe output from the machine learning model does not pass the auditthreshold (i.e., is flagged), processing may proceed to (238). In thisregard, at least a portion of the outputs having a higher confidencelevel may be additionally assessed by the additional device(s) 225.

At (238), the server computer 215 may determine whether and how to usethe additional devices 225 in a further assessment of the set of media.According to embodiments, the users of the additional devices 225 mayhave different licenses, qualifications, experience, and/or the like,and the assessment of the set of media (or more particularly, theproperty and characteristics thereof depicted in the set of media) mayrequire a user(s) having a specific license, qualification, experience,and/or the like. For example, if a vehicle depicted in the set of mediais registered to a policyholder in Illinois, there may be a requirementthat the users of the additional devices 225 be claim handlers with alicense in Illinois. As a further example, if a vehicle depicted in theset of media has an MSRP of over $150,000, there may be a requirementthat the users of the additional devices 225 have previous experienceassessing vehicles having a comparable MSRP.

The determination of (238) may also involve determining an amount ofadditional devices 225 needed for the additional assessment, where theamount may be based on such factors as the age, make, and/or model ofthe vehicle, the MSRP of the vehicle, the estimated amount of damageoutput by the machine learning model, the confidence level of the outputfrom the machine learning model, and/or other factors. For example, ifthe vehicle depicted in the set of media is less than two (2) years oldand has an estimated amount of damage greater than $15,000, the servercomputer 215 may determine that fifteen (15) additional devices 225 areneeded for the additional assessment. For further example, if thevehicle depicted in the set of media is over ten (10) years old and rearbumper damage, the server computer 215 may determine that three (3)additional devices 225 are needed for the additional assessment. Itshould be appreciated that the server computer 215 may additionallystore a set of rules that the server computer 215 may use to make thedetermination(s) of (238), where the set of rules may be default orconfigurable by an administrator.

After determining the additional devices 225, the server computer 215may transmit (240) the set of media to the determined additional devices225. It should be appreciated that the determined additional devices 225may or may not overlap with the additional devices 225 that initiallyassessed the training data. The transmission of the set of media to thedetermined additional devices 225 may include a request to assess orestimate an amount of damage to the property depicted in the set ofmedia, as well as additional information or instructions, such asinformation associated with the property, an output from the machinelearning model analysis, and/or other information. Additionally, theserver computer 215 may transmit all or a portion of the set of mediaoriginally transmitted by the user device 205.

The users of the determined additional devices 225 may use thedetermined additional devices 225 to assess (242) the set of media. Inparticular, the users may make necessary inputs or selections via thedetermined additional devices 225 to specify information associated withan assessment of the damage to the property depicted in the set ofmedia. For example, the users may input or specify a description of thedamage, damaged areas or portions of the property, an estimated cost torepair the damage, an estimated amount of time needed to repair thedamage, a type of service provider having expertise needed to repair thedamage, and/or other information.

The determined set of additional devices 225 may transmit (244)information associated with the assessment to the server computer 215.In particular, the determined set of additional devices 225 may transmita description of the damage, damaged areas or portions of the property,an estimated cost to repair the damage, an estimated amount of timeneeded to repair the damage, a type of service provider having expertiseneeded to repair the damage, and/or other information. The servercomputer 215 may update (246) the machine learning model according tothe assessment information received from the determined set ofadditional devices 225. In particular, the server computer 215 may add,to the machine learning model, at least a portion of the set of media,such as the set of media assessed by the determined set of additionaldevices 225. In situations in which the set of media is transmitted tothe additional device(s) 225 in response to being flagged via the auditthreshold, the server computer 215 may update the machine learning modelto reflect both the original machine learning model output and theassessment completed by the additional device(s) 225, such that anydifferences may be reconciled with the originally-determined confidencelevel.

Additionally, the server computer 215 may append any assessmentinformation to the set of media added to the machine learning model. Inembodiments, the server computer 215 may determine, from the receivedassessment information, an updated estimate of the amount of damage tothe vehicle (e.g., by averaging any received damage estimates, or byother calculations). Accordingly, the updated machine learning model mayaccurately reflect the information associated with the set of media asassessed by the users of the determined set of additional devices 225.

At (248), the server computer 215 may generate a work order to addressor handle the damage to the property that is depicted in the set ofmedia. In generating the work order, the server computer 215 mayidentify a service provider (i.e., the service provider 220) based onthe information included in the analysis by the machine learning modelor the assessment performed by the determined set of additional devices225. For example, the assessment by the determined set of additionaldevices 225 may indicate that a windshield replacement is necessary fora vehicle, and the server computer 215 may automatically identify amechanic that is able to replace the windshield. For further example,the analysis by the machine learning model may indicate that a vehicleof a particular make and model needs engine repair, and the servercomputer 215 may automatically identify a service provider thatspecializes in vehicles of the same make and model. According toembodiments, the server computer 215 may maintain, in memory, a databaseor records of service providers, as well as capabilities andspecialties, location, contact information, and/or other informationassociated with the service providers.

The server computer 215 may transmit (250) the work order to thedetermined service provider 220. Additionally, the server computer 215may notify (252) the user device of the assessment and the work order.In particular, the server computer 215 may transmit any informationresulting from the analysis by the machine learning model and/or theassessment by the determined set of additional devices 225, as well asan indication of the service provider 220 and the work order transmittedto the service provider 220. Accordingly, the owner or individualassociated with the damaged property may make arrangements with theservice provider 220 to address the damage. In an implementation, theuser device 205 may enable the owner or individual associated with thedamaged property to approve (or not approve) of the work order.

FIGS. 3A, 3B, 4A, and 4B depict example interfaces associated with thesystems and methods. In embodiments, the interfaces may be displayed bya computing device. Generally, the interfaces of FIGS. 3A and 3B may bedisplayed by a computing device associated with a service provider, suchas one of the service providers 120 as discussed with respect to FIG.1A. Further, the interfaces of FIGS. 4A and 4B may be displayed by acomputing device operated by a user having an association with aproperty (e.g., a policyholder) and/or who submits a set of mediadepicting the property. The interfaces may be accessed and reviewed by auser, where the user may make selections, submit modifications, orfacilitate other functionalities.

FIGS. 3A and 3B depict example interfaces associated with an electronicwork order that is submitted to a service provider, where the interfacesmay be displayed by an electronic device associated with the serviceprovider. Generally, a server computer, such as the server computer 115as discussed with respect to FIG. 1A, may determine the serviceprovider, generate a work order describing the property (e.g., avehicle) and damage to the property, and transmit the work order to thedetermined service provider.

FIG. 3A depicts an interface 300 describing the work order, whichindicates that a 2019 SUV has an estimated amount of damage of$12,000-$15,000, namely, a front bumper replacement, a windshieldreplacement, and engine damage. A user associated with the serviceprovider may review the interface 300 and select whether to accept (301)or reject (302) the work order. In embodiments, if the user rejects(302) the work order, the server computer may determine an alternativeservice provider and transmit the work order to the alternative serviceprovider.

If the user accepts (301) the work order, the computing device maydisplay an interface 305 as depicted in FIG. 3B. The interface 305 mayenable the user to input an availability date for completion of the job,in particular via a month selection 308 and a day selection 309. Theinterface 305 may also enable the user to navigate back (306) to theinterface 300 of FIG. 3A. Additionally, the interface 305 includes anokay selection 307 to enable the user to confirm or accept the workorder with the entered job completion date. After the user selects theokay selection 307, the computing device may transmit the acceptance tothe server computer.

FIG. 4A depicts an interface 400 describing an authorization for a workorder. According to embodiments, in response to the server computerreceiving a work order acceptance from the service provider, the servercomputer may generate and transmit the work order authorization to acomputing device operated by the user having an association with theproperty. The interface 400 may describe the property and the estimateddamage to the property, along with an indication that the work order wassubmitted to ABC Mechanics in Bloomington, Ill. with an estimatedcompletion date of June 15. The user may review the interface 400 andselect whether to accept (401) or reject (402) the work orderauthorization. In embodiments, if the user rejects (402) the work orderauthorization, the server computer may determine an alternative serviceprovider and attempt to facilitate acceptance of a work order betweenthe user and the alternative service provider.

If the user accepts (401) the work order authorization, the computingdevice may display an interface 405 as depicted in FIG. 4B. Theinterface 405 may indicate that the work order is confirmed with ABCMechanics in Bloomington, Ill., and may instruct the user to transportthe vehicle to ABC Mechanics. The user may select to dismiss theinterface 405 via an okay selection 406.

FIG. 5 depicts is a block diagram of an example method 500 for using amachine learning model to automatically facilitate work orders. Themethod 500 may be facilitated by an electronic device (such as theserver computer 115 or components associated with the media analysisplatform as discussed with respect to FIGS. 1A and 1B) that may be incommunication with additional devices and/or data sources.

The method 500 may begin when the electronic device receives (block 505)a set of media depicting a property. In embodiments, the electronicdevice may receive the set of media from one or more user devicesoperated by one or more users, or from another data source, where theset of media may be a set of digital images and/or videos that visuallydepict the property (e.g., a vehicle) and damage or potential damage tothe property.

The electronic device may analyze the set of media data to determine(block 510) (i) an amount of damage to the property, and (ii) a type ofdamage to the property. In embodiments, the electronic device mayanalyze the set of media data using a machine learning model that may asupervised or unsupervised machine learning model. If the machinelearning model is supervised, the machine learning model may be trainedusing a set of labels associated with a set of training media, where theset of labels may be specified by a set of individuals operating a setof additional devices. The determined amount of damage to the propertyand the type of damage to the property may be embodied as an output fromthe machine learning model analysis.

The electronic device may assess (block 515) a confidence level for theamount of damage. Additionally, the electronic device may determine(block 520) whether the confidence level at least meets a thresholdlevel. In embodiments, the threshold level may be based on the amount ofdamage to the property depicted in the set of media, and/or based onother information (e.g., an age of the property), such that theelectronic device may compare the confidence level to the appropriatethreshold level. Additionally or alternatively, the electronic devicemay flag the output from the machine learning model analysis accordingto an audit threshold (e.g., 5%), where the electronic device may applythe audit threshold when the confidence level at least meets a certainthreshold (e.g., 95%).

If the confidence level at least meets the threshold level (“YES”) and,optionally, if the output is not flagged by the audit threshold,processing may proceed to block 550. If the confidence level does not atleast meet the threshold level (“NO”) or, optionally, if the output isflagged by the audit threshold, the electronic device may determine(block 525) a set of individuals needed to manually assess the set ofmedia. In embodiments, the electronic device may also determine anamount of individuals needed in the set of individuals, which may bebased on at least one of an age of the property, a value of theproperty, or another metric or parameter. Additionally or alternatively,the electronic device may determine the set of individuals based on eachof the set of individuals having a certain qualification or license toassess damage to the property.

The electronic device may avail (block 530) the set of media for reviewby the set of individuals. In embodiments, the electronic device maytransmit, to a set of additional electronic devices associated with theset of individuals, at least a portion of the set of media andinformation descriptive of the property. The set of additionalelectronic devices may be additional devices configured to presentcontent (e.g., the portion of the set of media and the informationdescriptive of the property) and receive selections from the set ofindividuals (e.g., an assessment of the damage depicted in the portionof the set of media).

The electronic device may receive (block 535) a set of assessments ofthe set of media by the set of individuals. In embodiments, theelectronic device may receive the set of assessments from the set ofadditional electronic devices (e.g., the additional devices) associatedwith the set of individuals. The electronic device may calculate (block540) an updated amount of damage to the property based on the receivedset of assessments. In embodiments, each of the set of assessments mayindicate an assessed amount of damage to the property, and theelectronic device may calculate the updated amount of damage as anaverage of the set of assessed amount of damage indicated in the set ofassessments. It should be appreciated that other calculations of theupdated amount of damage are envisioned.

The electronic device may update (block 545) the machine learning modelwith the updated amount of damage. In embodiments, the electronic devicemay update the machine learning model by associating at least a portionof the set of media with the updated amount of damage and informationdescriptive of the property. In this regard, the machine learning modelmay be improved and may be subsequently used by the electronic devicefor more accurate property assessment.

The electronic device may determine (block 550) a service providerequipped to address the type of damage to the property. In embodiments,the electronic device may determine the service provider based on atleast one of a location of the electronic device, the updated amount ofdamage to the property, an age of the property, a type of the property,or other metrics or characteristics.

The electronic device may transmit (block 555), to the service provider,an electronic work order request indicating the updated amount of damageand the type of damage, where the service provide may select whether toaccept or reject the electronic work order request. Additionally, theelectronic device may transmit, to an appropriate user device (e.g., adevice operated by a user to originally submitted the set of media), anotification indicating the electronic work order and the serviceprovider, wherein the electronic device enables the user to select toconfirm or reject the electronic work order.

Although the following text sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the invention may be defined by the words of the claims setforth at the end of this patent. The detailed description is to beconstrued as exemplary only and does not describe every possibleembodiment, as describing every possible embodiment would beimpractical, if not impossible. One could implement numerous alternateembodiments, using either current technology or technology developedafter the filing date of this patent, which would still fall within thescope of the claims.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a non-transitory, machine-readable medium) or hardware. In hardware,the routines, etc., are tangible units capable of performing certainoperations and may be configured or arranged in a certain manner. Inexample embodiments, one or more computer systems (e.g., a standalone,client or server computer system) or one or more hardware modules of acomputer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware module that operates to perform certain operations asdescribed herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that may be permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that may betemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules may provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it may becommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and may operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a home environment, anoffice environment, or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment may be included in at leastone embodiment. The appearances of the phrase “in one embodiment” invarious places in the specification are not necessarily all referring tothe same embodiment.

As used herein, the terms “comprises,” “comprising,” “may include,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the description. Thisdescription, and the claims that follow, should be read to include oneor at least one and the singular also may include the plural unless itis obvious that it is meant otherwise.

This detailed description is to be construed as examples and does notdescribe every possible embodiment, as describing every possibleembodiment would be impractical.

What is claimed is:
 1. A computer-implemented method of using a machinelearning model to automatically facilitate work orders, the methodcomprising: receiving, from an electronic device, a set of mediadepicting a property; analyzing, by a processor using a machine learningmodel, the set of media, including: determining (i) an amount of damageto the property depicted in the set of media, and (ii) a type of damageto the property depicted in the set of media, determining a confidencelevel for the amount of damage; and determining the confidence level isbelow a threshold level; in response to the determination that theconfidence level for the amount of damage is below the threshold level,availing the set of media for manual review, including: determining,based on the property, a set of individuals needed to manually assessthe set of media, each of the set of individuals having a qualification,providing the set of media for review by the set of individuals,receiving a set of assessments of the set of media by the set ofindividuals, each assessment of the set of assessments comprising anestimated amount of damage to the property, determining an updatedamount of damage to the property based on the set of assessments,receiving a set of labels associated with a set of training media, theset of labels specified by the set of individuals, and training themachine learning model based at least in part on the set of labels andthe updated amount of damage to the property; determining, by theprocessor, a service provider equipped to address the type of damage tothe property; and transmitting, to the service provider via a computernetwork, an electronic work order request indicating the updated amountof damage and the type of damage.
 2. The computer-implemented method ofclaim 1, wherein determining the set of individuals needed to manuallyassess the set of media comprises: determining the set of individuals,each of the set of individuals having a license to assess damage to theproperty.
 3. The computer-implemented method of claim 1, furthercomprising: determining, based on at least one of an age of the propertyor a value of the property, an amount of individuals needed in the setof individuals.
 4. The computer-implemented method of claim 1, furthercomprising: transmitting, to the electronic device, a notificationindicating the electronic work order and the service provider, whereinthe electronic device enables a user to select to confirm the electronicwork order.
 5. The computer-implemented method of claim 1, whereindetermining the updated amount of damage to the property based on theset of assessments comprises: determining an average estimated amount ofdamage indicated in the set of assessments, wherein the average is theupdated amount of damage.
 6. The computer-implemented method of claim 1,wherein providing the set of media for review by the set of individualscomprises: transmitting, to a set of additional electronic devicesassociated with the set of individuals, at least a portion of the set ofmedia and information descriptive of the property.
 7. Thecomputer-implemented method of claim 1, wherein training the machinelearning model with the updated amount of damage to the propertycomprises: updating the machine learning model by associating at least aportion of the set of media with the updated amount of damage andinformation descriptive of the property.
 8. The computer-implementedmethod of claim 1, wherein determining the service provider equipped toaddress the type of damage to the property comprises: determining, basedon at least one of a location of the electronic device, the updatedamount of damage to the property, an age of the property, and a type ofthe property, the service provide equipped to address the type of damageto the property.
 9. The computer-implemented method of claim 1, whereindetermining the confidence level comprises: determining the confidencelevel for the amount of damage, wherein the confidence level at leastmeets an audit threshold level, and flagging the set of media forfurther review.
 10. A system for automatically facilitating work orders,the system comprising: a transceiver; a memory storing a set ofinstructions and a machine learning model; and a processor interfacedwith the transceiver and the memory, and configured to execute the setof instructions to cause the processor to: receive, from an electronicdevice via the transceiver, a set of media depicting a property,analyze, using the machine learning model, the set of media, including:determining (i) an amount of damage to the property depicted in the setof media, and (ii) a type of damage to the property depicted in the setof media, determining a confidence level for the amount of damage, anddetermining the confidence level is below a threshold level; in responseto the determination that the confidence level for the amount of damageis below a threshold level, avail the set of media for manual review,including: determining, based on the property, a set of individualsneeded to manually assess the set of media, each of the set ofindividuals having a qualification, providing the set of media forreview by the set of individuals, receiving a set of assessments of theset of media by the set of individuals, each assessment of the set ofassessments comprising an estimated amount of damage to the property,determining an updated amount of damage to the property based on the setof assessments, and receiving a set of labels associated with a set oftraining media, the set of labels specified by the set of individuals,and training the machine learning model based at least in part on theset of labels and the updated amount of damage to the property;determine a service provider equipped to address the type of damage tothe property, and transmit, to the service provider via the transceiver,an electronic work order request indicating the updated amount of damageand the type of damage.
 11. The system of claim 10, wherein to determinethe set of individuals needed to manually assess the set of media, theprocessor is configured to: determine the set of individuals, each ofthe set of individuals having a license to assess damage to theproperty.
 12. The system of claim 10, wherein the processor is furtherconfigured to: determine, based on at least one of an age of theproperty or a value of the property, an amount of individuals needed inthe set of individuals.
 13. The system of claim 10, wherein theprocessor is further configured to: transmit, to the electronic devicevia the transceiver, a notification indicating the electronic work orderand the service provider, wherein the electronic device enables a userto select to confirm the electronic work order.
 14. The system of claim10, wherein to determine the updated amount of damage to the propertybased on the set of assessments, the processor is configured to:determine an average estimated amount of damage indicated in the set ofassessments, wherein the average is the updated amount of damage. 15.The system of claim 10, wherein to avail the set of media for review bythe set of individuals, the processor is configured to: transmit, to aset of additional electronic devices associated with the set ofindividuals, at least a portion of the set of media and informationdescriptive of the property.
 16. The system of claim 10, wherein totrain the machine learning model with the updated amount of damage tothe property, the processor is configured to: update, in the memory, themachine learning model by associating at least a portion of the set ofmedia with the updated amount of damage and information descriptive ofthe property.
 17. The system of claim 10, wherein the processordetermines the service provider equipped to address the type of damageto the property based on at least one of a location of the electronicdevice, the updated amount of damage to the property, an age of theproperty, and a type of the property.
 18. The system of claim 10,wherein the confidence level at least meets an audit threshold level,and wherein the processor is further configured to: flag the set ofmedia for further review.